{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "132f5806",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "#  Mplfinance Used To Plot Awesome Oscillator\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f21455df",
   "metadata": {},
   "source": [
    "### What Is The Awesome Oscillator\n",
    "- The aptly named Awesome Oscillator is an amazing  designed to measure the underlying  as well as to confirm trends and anticipate reversals.\n",
    "\n",
    "- The Awesome Oscillator was developed by the legendary chartist Bill Williams, who described it as the ‘best momentum indicator’ that is ‘as simple as it is elegant’.\n",
    "\n",
    "- The Awesome Oscillator is based on a combination of , but its ‘awesomeness’ is illustrated by the clear and straightforward  that it generates.\n",
    "\n",
    "- As its name suggests, the Awesome Oscillator belongs to the broader group of oscillators, which consists of indicators such as the ,  and . But while most oscillators usually swing between defined values such as ‘0 to 100’ or ‘-100 to +100’, the Awesome Oscillator is unbounded.\n",
    "\n",
    "- Other popular  include the Accelerator Oscillator, Fractals, Gator Oscillator, Alligator and the Market Facilitation Index.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d2bc743",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "### mplfinance 'yahoo' styles was used to customize Awesome Oscillator:\n",
    "- Type of Plot Use `candle`\n",
    "- Awesome Oscillator Build With Histogram Bar\n",
    "- Background, Grid, and Figure Colors\n",
    "- Grid style\n",
    "- Y-Axis On The Right or Left\n",
    "- Matplotlib Defaults\n",
    "- panel\n",
    "- Bar Color\n",
    "#### The simplest way to do this is to choose one of the `add_plot` that come packaged with `mplfinance`\n",
    "#### but, as we see below, it is also possible to customize your own `mplfinance styles`.\n",
    "#### Also Other Plot Type Can Be Used\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8db9cbc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import mplfinance as mpf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "976e1224",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This allows multiple outputs from a single jupyter notebook cell:\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0a501a1",
   "metadata": {},
   "source": [
    "### Read in daily data for the S&P 500 from November of 2019 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "760edd72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 6)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2003-06-25</th>\n",
       "      <td>20.530001</td>\n",
       "      <td>20.83</td>\n",
       "      <td>19.99</td>\n",
       "      <td>20.040001</td>\n",
       "      <td>13.693501</td>\n",
       "      <td>61250600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-06-26</th>\n",
       "      <td>20.299999</td>\n",
       "      <td>20.76</td>\n",
       "      <td>20.15</td>\n",
       "      <td>20.629999</td>\n",
       "      <td>14.096654</td>\n",
       "      <td>52904900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Open   High    Low      Close  Adj Close    Volume\n",
       "Date                                                               \n",
       "2003-06-25  20.530001  20.83  19.99  20.040001  13.693501  61250600\n",
       "2003-06-26  20.299999  20.76  20.15  20.629999  14.096654  52904900"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-04-07</th>\n",
       "      <td>28.08</td>\n",
       "      <td>28.129999</td>\n",
       "      <td>27.480000</td>\n",
       "      <td>27.620001</td>\n",
       "      <td>18.923342</td>\n",
       "      <td>72680200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-04-08</th>\n",
       "      <td>28.08</td>\n",
       "      <td>28.139999</td>\n",
       "      <td>27.200001</td>\n",
       "      <td>27.370001</td>\n",
       "      <td>18.752058</td>\n",
       "      <td>71791400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open       High        Low      Close  Adj Close    Volume\n",
       "Date                                                                   \n",
       "2004-04-07  28.08  28.129999  27.480000  27.620001  18.923342  72680200\n",
       "2004-04-08  28.08  28.139999  27.200001  27.370001  18.752058  71791400"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idf = pd.read_csv('../data/yahoofinance-INTC-19950101-20040412.csv',index_col=0,parse_dates=True).tail(200)\n",
    "\n",
    "df = idf.copy()\n",
    "df.index.name = 'Date'\n",
    "df.shape\n",
    "df.head(2)\n",
    "df.tail(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2859d3fe",
   "metadata": {},
   "source": [
    "---\n",
    "- **Calculating the Awesome Indicator**\n",
    "- The formula to calculate the Awesome Oscillator is as follows:\n",
    "\n",
    "- Awesome Oscillator = SMA (MEDIAN PRICE, 5)-SMA (MEDIAN PRICE, 34)\n",
    "\n",
    "- Where:\n",
    "\n",
    "- SMA = simple moving average\n",
    "\n",
    "- Median Price = (HIGH+LOW)/2\n",
    "- **Here is Function For The Calculation:**\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0294b569",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculation For Simple Moving Average For Length 34 as Long SMA\n",
    "df['SMA34'] = df['Close'].rolling(window=34).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "60577083",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculation For Simple Moving Average For Length 5 as Short SMA\n",
    "df['SMA5'] = df['Close'].rolling(window=5).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8f51c335",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Awesome_Oscillator Column Assign To df\n",
    "df['AO'] = df['SMA5'] - df['SMA34']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ed037fa",
   "metadata": {},
   "source": [
    "### Function For Generate Color Awesome_oscillator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1ad7fb7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_awesome_oscillator_color(df):\n",
    "    awesome_oscillator_color = []\n",
    "    awesome_oscillator_color.clear()\n",
    "    for i in range (0,len(df[\"AO\"])):\n",
    "        if df[\"AO\"][i] >= 0 and df[\"AO\"][i-1] < df[\"AO\"][i]:\n",
    "            awesome_oscillator_color.append('#26A69A')\n",
    "            #print(i,'green')\n",
    "        elif df[\"AO\"][i] >= 0 and df[\"AO\"][i-1] > df[\"AO\"][i]:\n",
    "            awesome_oscillator_color.append('#FF5252')\n",
    "            #print(i,'faint green')\n",
    "        elif df[\"AO\"][i] < 0 and df[\"AO\"][i-1] > df[\"AO\"][i] :\n",
    "            #print(i,'red')\n",
    "            awesome_oscillator_color.append('#FF5252')\n",
    "        elif df[\"AO\"][i] < 0 and df[\"AO\"][i-1] < df[\"AO\"][i] :\n",
    "            #print(i,'faint red')\n",
    "            awesome_oscillator_color.append('#26A69A')\n",
    "        else:\n",
    "            awesome_oscillator_color.append('#000000')\n",
    "    return awesome_oscillator_color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "244270b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of Color Assiging To Awesome Oscillator\n",
    "awesome_oscillator_color = generate_awesome_oscillator_color(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a12c51b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Extracted And New Variable Applied\n",
    "awesome_oscillator = df[['AO']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c56fba65",
   "metadata": {},
   "source": [
    "Let's add a new panel containing Histogram.\n",
    "\n",
    "By default addplot uses Panel 0 For Main Candlestick."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9febb60d",
   "metadata": {},
   "outputs": [],
   "source": [
    "ao = [\n",
    "    mpf.make_addplot(awesome_oscillator,type='bar',width=0.7,color=awesome_oscillator_color,panel=1,alpha=1,secondary_y=True)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2b47e643",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2000x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpf.plot(\n",
    "    df,\n",
    "    volume=True,\n",
    "    volume_panel = 2,\n",
    "    type=\"candle\", \n",
    "    style=\"yahoo\",\n",
    "    addplot=ao,\n",
    "    figsize=(20,10)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "939d0172",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
